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-micro-expression-recognition-based-on-spatiotemporal-features

Micro-expression recognition based on spatiotemporal features

afartoolbox

AFAR: A Deep Learning Based Toolbox for Automated Facial Affect Recognition

akmnet-micro-expression

Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining

alphatree-graphic-deep-neural-network

机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱

au_r-cnn

The official implementation code of paper: "AU R-CNN：Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection".

awesome-fer

🔆 Top conferences & Journals focused on Facial expression recognition (FER)/ Facial action unit (FAU) 💫 ✨

awesome-few-shot-learning

A curated list of resources about few-shot and one-shot learning

awesome-java

Collection of awesome Java project on Github(非常棒的 Java 开源项目集合).

awesome-meta-learning

A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.

awesome-papers-fewshot

Collection for Few-shot Learning

awesome-zero-shot-learning

A curated list of papers, code and resources pertaining to zero shot learning

biometric-face-recognition-using-autoencoders

Neural network algorithm is used to extract features from face images. And stacked network is created using auto encoders and softmax layer is used for classification.

blog

基于springboot的个人博客，技术栈：mybatis，mysql，elasticsearch，thymeleaf等

blog-1

vue + springboot 前后端分离博客

blog_admin

博客后台管理系统前端项目

cada-vae-pytorch

Pytorch implementation of the paper "Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders" (CVPR 2019)

capsnet-pytorch

Pytorch implementation for NIPS2017 paper `Dynamic Routing Between Capsules`

ccpd

[ECCV 2018] CCPD & PDRC: a diverse and well-annotated dataset for license plate detection and recognition

challenge-condition-fer-dataset

This is our collected datasets for challenge condition facial expression recognition

chasing-after-ai

cpp-design-patterns

C++设计模式

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

dataease

人人可用的开源数据可视化分析工具。

deep-emotion

Facial Expression Recognition Using Attentional Convolutional Network, Pytorch implementation

deep-learning-for-expression-recognition-in-image-sequences

The project uses state of the art deep learning on collected data for automatic analysis of emotions.

deep_learning_object_detection

A paper list of object detection using deep learning.

deeplearning-500-questions

（forked from scutan90/DeepLearning-500-questions）深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

ebook

一些已经读、正在读、将要读的书籍

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emotion-fan

ICIP 2019: Frame Attention Networks for Facial Expression Recognition in Videos